class_docstr: Implementation of a lemmatizer based on a
  dictionary of lemmas and forms, backing off to regex rules.
  Since a given form may map to multiple lemmas,
  a corpus-based frequency disambiguator is employed.
  
  Subclasses must provide methods to load dictionary and corpora,
  and to specify regular expressions.
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docstr: Lemmatize tokens in a list of strings.
  
  >>> from cltk.lemmatize.ang import OldEnglishDictionaryLemmatizer
  >>> lemmatizer = OldEnglishDictionaryLemmatizer()
  >>> lemmatizer.lemmatize(['eotenas','ond','ylfe','ond','orcneas'], return_frequencies=True, best_guess=True)
  [('eoten', -9.227295812625597), ('and', -2.8869365088978443), ('ylfe', -9.227295812625597), ('and', -2.8869365088978443), ('orcneas', -9.227295812625597)]
docstr_tok: ["Lemmatize","tokens","in","a","list","of","strings",".",">",">",">","from","cltk.lemmatize.ang","import","OldEnglishDictionaryLemmatizer",">",">",">","lemmatizer","=","OldEnglishDictionaryLemmatizer","(",")",">",">",">","lemmatizer.lemmatize","(","[","'eotenas","'",",","'ond","'",",","'ylfe","'",",","'ond","'",",","'orcneas","'","]",",","return_frequencies=True",",","best_guess=True",")","[","(","'eoten","'",",","-9.227295812625597",")",",","(","'and","'",",","-2.8869365088978443",")",",","(","'ylfe","'",",","-9.227295812625597",")",",","(","'and","'",",","-2.8869365088978443",")",",","(","'orcneas","'",",","-9.227295812625597",")","]"]
code: def lemmatize(
      self,
      tokens: List[str],
      best_guess: bool = True,
      return_frequencies: bool = False,
  ) -> Union[str, List[Union[str, Tuple[str, float]]]]:
      """
      Lemmatize tokens in a list of strings.
  
      >>> from cltk.lemmatize.ang import OldEnglishDictionaryLemmatizer
      >>> lemmatizer = OldEnglishDictionaryLemmatizer()
      >>> lemmatizer.lemmatize(['eotenas','ond','ylfe','ond','orcneas'], return_frequencies=True, best_guess=True)
      [('eoten', -9.227295812625597), ('and', -2.8869365088978443), ('ylfe', -9.227295812625597), ('and', -2.8869365088978443), ('orcneas', -9.227295812625597)]
      """
  
      return [
          self.lemmatize_token(token, best_guess, return_frequencies)
          for token in tokens
      ]
  
file_path: cltk/src/cltk/lemmatize/naive_lemmatizer.py
imports: import math
  import os
  import re
  from abc import ABC, abstractmethod
  from typing import List, Tuple, Union
  from numpy import argmax
  from cltk.utils import CLTK_DATA_DIR
label_desc: Use image node_id 8 for calling the DictionaryRegexLemmatizer obj's underlying member method code with example usage: obj.lemmatize(tokens, best_guess, return_frequencies) and returns: unknown